---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r2a2d0.15-1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# sentiment-lora-r2a2d0.15-1

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3633
- Accuracy: 0.8396
- Precision: 0.8128
- Recall: 0.7890
- F1: 0.7992

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5664        | 1.0   | 122  | 0.5221          | 0.7218   | 0.6580    | 0.6432 | 0.6487 |
| 0.5148        | 2.0   | 244  | 0.5111          | 0.7243   | 0.6758    | 0.6899 | 0.6810 |
| 0.4924        | 3.0   | 366  | 0.4791          | 0.7444   | 0.6884    | 0.6741 | 0.6799 |
| 0.4615        | 4.0   | 488  | 0.4651          | 0.7644   | 0.7148    | 0.7058 | 0.7099 |
| 0.4516        | 5.0   | 610  | 0.4581          | 0.7644   | 0.7214    | 0.7408 | 0.7286 |
| 0.4291        | 6.0   | 732  | 0.4295          | 0.7895   | 0.7462    | 0.7385 | 0.7421 |
| 0.4194        | 7.0   | 854  | 0.4191          | 0.7995   | 0.7581    | 0.7606 | 0.7593 |
| 0.3994        | 8.0   | 976  | 0.4048          | 0.8120   | 0.7745    | 0.7645 | 0.7691 |
| 0.3919        | 9.0   | 1098 | 0.3950          | 0.8246   | 0.7954    | 0.7659 | 0.7778 |
| 0.3762        | 10.0  | 1220 | 0.3881          | 0.8271   | 0.8022    | 0.7626 | 0.7777 |
| 0.3704        | 11.0  | 1342 | 0.3806          | 0.8271   | 0.7949    | 0.7776 | 0.7853 |
| 0.3642        | 12.0  | 1464 | 0.3733          | 0.8421   | 0.8122    | 0.8008 | 0.8061 |
| 0.3614        | 13.0  | 1586 | 0.3753          | 0.8321   | 0.8092    | 0.7687 | 0.7842 |
| 0.3474        | 14.0  | 1708 | 0.3695          | 0.8396   | 0.8155    | 0.7840 | 0.7969 |
| 0.3479        | 15.0  | 1830 | 0.3675          | 0.8421   | 0.8142    | 0.7958 | 0.8040 |
| 0.3347        | 16.0  | 1952 | 0.3649          | 0.8421   | 0.8142    | 0.7958 | 0.8040 |
| 0.335         | 17.0  | 2074 | 0.3653          | 0.8371   | 0.8114    | 0.7822 | 0.7943 |
| 0.3361        | 18.0  | 2196 | 0.3632          | 0.8396   | 0.8128    | 0.7890 | 0.7992 |
| 0.3343        | 19.0  | 2318 | 0.3636          | 0.8371   | 0.8114    | 0.7822 | 0.7943 |
| 0.3347        | 20.0  | 2440 | 0.3633          | 0.8396   | 0.8128    | 0.7890 | 0.7992 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2